Fusing Simultaneous EEG-fMRI by Linking Multivariate Classifiers

Multivariate pattern analysis (MVPA) has typically been used in neuroimaging to draw inferences from a single modality, e.g., functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). As simultaneous acquisition of different neuroimaging modalities becomes more common, one consideration is how to apply MVPA
methods to analyze the resulting multimodal dataspaces. We present a multi-modal fusion technique that seeks to simultaneously train a multivariate classifier and identify correlated components across the two modalities. We validate our approach on a real simultaneous EEG-fMRI dataset.